A taxonomy of latent structure assumptions for probability matrix decomposition models

DOI10.1007/BF02296653zbMath1306.62478MaRDI QIDQ2259550

Michel Meulders, Paul De Boeck, Iven van Mechelen

Publication date: 4 March 2015

Published in: Psychometrika (Search for Journal in Brave)

zbMATH Keywords

Bayesian analysisdiscrete datadata augmentationmatrix decompositionpsychometricsposterior predictive check

Mathematics Subject Classification ID

Applications of statistics to psychology (62P15)

Related Items (2)

Constrained multilevel latent class models for the analysis of three-way three-mode binary data ⋮ Bayesian hierarchical classes analysis

Uses Software

• BUGS
• BayesDA
• bootstrap

Cites Work

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• Estimating the dimension of a model
• Inference from iterative simulation using multiple sequences
• Posterior predictive \(p\)-values
• Estimating multiple classification latent class models
• Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
• Sampling-Based Approaches to Calculating Marginal Densities
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• The Calculation of Posterior Distributions by Data Augmentation
• Dealing With Label Switching in Mixture Models
• Computational and Inferential Difficulties with Mixture Posterior Distributions
• P Values for Composite Null Models
• Asymptotic Distribution of P Values in Composite Null Models
• Exploratory latent structure analysis using both identifiable and unidentifiable models
• Probabilistic Feature Analysis of Facial Perception of Emotions
• Probability matrix decomposition models and main-effects generalized linear models for the analysis of replicated binary associations.
• A new look at the statistical model identification

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